Search Results for author: Masashi Yoshikawa

Found 11 papers, 5 papers with code

Tracing and Manipulating Intermediate Values in Neural Math Problem Solvers

1 code implementation17 Jan 2023 Yuta Matsumoto, Benjamin Heinzerling, Masashi Yoshikawa, Kentaro Inui

Previous research has shown that information about intermediate values of these inputs can be extracted from the activations of the models, but it is unclear where that information is encoded and whether that information is indeed used during inference.

Multimodal Logical Inference System for Visual-Textual Entailment

no code implementations ACL 2019 Riko Suzuki, Hitomi Yanaka, Masashi Yoshikawa, Koji Mineshima, Daisuke Bekki

A large amount of research about multimodal inference across text and vision has been recently developed to obtain visually grounded word and sentence representations.

Automated Theorem Proving Natural Language Inference +1

Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation

no code implementations ACL 2019 Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima, Daisuke Bekki

We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees.

Domain Adaptation

Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference

1 code implementation15 Nov 2018 Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, Daisuke Bekki

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data.

Knowledge Base Completion Natural Language Inference +1

Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning

no code implementations NAACL 2018 Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, Daisuke Bekki

In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas.

Automated Theorem Proving Formal Logic +3

A* CCG Parsing with a Supertag and Dependency Factored Model

1 code implementation ACL 2017 Masashi Yoshikawa, Hiroshi Noji, Yuji Matsumoto

Our model achieves the state-of-the-art results on English and Japanese CCG parsing.

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